# Library for handling data
import pandas as pd

# Library used to split data into training and testing sets
from sklearn.model_selection import train_test_split

# Library for measuring accuracy of ML models
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix

# Machine Learning model used
from sklearn.ensemble import RandomForestClassifier

# Library for visualization
import plotly.express as px

df = pd.read_csv('pbta_miRactivity_results.csv')
df

	Kids_First_Biospecimen_ID	sample_id	aliquot_id	RNA_library	short_histology	molecular_subtype	age_at_diagnosis_days	reported_gender	MIMAT0010195	MIMAT0004481	...	MIMAT0004986	MIMAT0004987	MIMAT0000094	MIMAT0026473	MIMAT0004510	MIMAT0000095	MIMAT0022842	MIMAT0000097	MIMAT0004678	MIMAT0000689
0	BS_GXTFW99H	7316-2151	740006	stranded	HGAT	HGG, H3 wildtype	2166	Male	0.203215	0.170353	...	0.163825	0.141134	0.192549	0.153315	0.147092	0.299865	0.134360	0.044772	0.167571	0.105896
1	BS_HF3RNN2T	7316-2155	564491	stranded	Medulloblastoma	MB, Group4	3606	Female	0.183178	0.246241	...	0.153730	0.147374	0.322979	0.119636	0.139716	0.185569	0.173047	0.180138	0.172517	0.151552
2	BS_MMNRZ1EQ	7316-2141	571446	stranded	ATRT	NaN	383	Male	0.056398	-0.019984	...	-0.130431	-0.140412	-0.111823	-0.140318	-0.142191	-0.041198	-0.148094	-0.080920	-0.018398	0.060411
3	BS_53GQKVEX	7316-487	588341	stranded	LGAT	LGG, KIAA1549-BRAF	553	Male	0.169361	0.223773	...	0.145929	0.145370	0.298415	0.121007	0.138993	0.214281	0.166954	0.166200	0.155085	0.144268
4	BS_JS95PE0J	7316-1774	574549	stranded	HGAT	HGG, H3 wildtype, TP53 loss	2431	Male	0.132491	0.170572	...	0.144303	0.113045	0.219452	0.115481	0.124098	0.231750	0.119749	-0.039174	0.145273	0.000353
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
239	BS_RCFQ31XF	7316-873	570111	stranded	Ependymoma	EPN, To be classified	442	Male	0.157141	0.125819	...	0.086446	0.070290	0.126832	0.068954	0.074025	0.174126	0.074397	0.061975	0.110112	0.136656
240	BS_QXKRN6CR	7316-111	470423	stranded	Ependymoma	EPN, ST RELA	4519	Female	0.146616	0.118695	...	0.045809	0.035370	0.069957	0.042077	0.038712	0.148805	0.029867	0.066198	0.084429	0.123514
241	BS_4FZS7TX4	7316-160	549587	stranded	Ependymoma	EPN, To be classified	1223	Male	0.194594	0.116444	...	0.070706	0.075477	0.076592	0.069451	0.078228	0.165159	0.073962	0.113423	0.113134	0.197433
242	BS_XQ5SFW35	7316-89	577715	stranded	HGAT	HGG, H3 wildtype	1461	Male	0.107664	0.102398	...	0.100223	0.099694	0.141058	0.088554	0.105310	0.213635	0.098756	0.099611	0.059582	0.113228
243	BS_GS1KR5PK	7316-1772	717124	stranded	Medulloblastoma	MB, SHH	1221	Male	0.209188	0.094796	...	0.036446	0.036185	0.015965	0.039582	0.038798	0.153996	0.027754	0.072525	0.116735	0.214161

244 rows × 2058 columns

hdf = df.drop(['Kids_First_Biospecimen_ID', 'sample_id', 'aliquot_id', 'RNA_library', 'molecular_subtype', 'age_at_diagnosis_days', 'reported_gender'], axis=1)
hdf

	short_histology	MIMAT0010195	MIMAT0004481	MIMAT0000062	MIMAT0000063	MIMAT0026472	MIMAT0000064	MIMAT0004484	MIMAT0000065	MIMAT0004485	...	MIMAT0004986	MIMAT0004987	MIMAT0000094	MIMAT0026473	MIMAT0004510	MIMAT0000095	MIMAT0022842	MIMAT0000097	MIMAT0004678	MIMAT0000689
0	HGAT	0.203215	0.170353	0.044772	0.100227	0.228451	0.044772	0.118220	0.044772	0.013709	...	0.163825	0.141134	0.192549	0.153315	0.147092	0.299865	0.134360	0.044772	0.167571	0.105896
1	Medulloblastoma	0.183178	0.246241	0.180138	0.162924	0.157944	0.180138	0.201304	0.180138	0.155110	...	0.153730	0.147374	0.322979	0.119636	0.139716	0.185569	0.173047	0.180138	0.172517	0.151552
2	ATRT	0.056398	-0.019984	-0.080920	-0.121344	-0.050051	-0.080920	-0.097578	-0.080920	0.017420	...	-0.130431	-0.140412	-0.111823	-0.140318	-0.142191	-0.041198	-0.148094	-0.080920	-0.018398	0.060411
3	LGAT	0.169361	0.223773	0.166200	0.160803	0.155500	0.166200	0.206727	0.166200	0.156179	...	0.145929	0.145370	0.298415	0.121007	0.138993	0.214281	0.166954	0.166200	0.155085	0.144268
4	HGAT	0.132491	0.170572	-0.039174	-0.012450	0.191062	-0.039174	0.025425	-0.039174	0.029123	...	0.144303	0.113045	0.219452	0.115481	0.124098	0.231750	0.119749	-0.039174	0.145273	0.000353
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
239	Ependymoma	0.157141	0.125819	0.061975	0.082500	0.131669	0.061975	0.106920	0.061975	0.106418	...	0.086446	0.070290	0.126832	0.068954	0.074025	0.174126	0.074397	0.061975	0.110112	0.136656
240	Ependymoma	0.146616	0.118695	0.066198	0.063461	0.127475	0.066198	0.096739	0.066198	0.086737	...	0.045809	0.035370	0.069957	0.042077	0.038712	0.148805	0.029867	0.066198	0.084429	0.123514
241	Ependymoma	0.194594	0.116444	0.113423	0.120964	0.112428	0.113423	0.143909	0.113423	0.159605	...	0.070706	0.075477	0.076592	0.069451	0.078228	0.165159	0.073962	0.113423	0.113134	0.197433
242	HGAT	0.107664	0.102398	0.099611	0.126305	0.113148	0.099611	0.160924	0.099611	0.100055	...	0.100223	0.099694	0.141058	0.088554	0.105310	0.213635	0.098756	0.099611	0.059582	0.113228
243	Medulloblastoma	0.209188	0.094796	0.072525	0.093812	0.118819	0.072525	0.119084	0.072525	0.168033	...	0.036446	0.036185	0.015965	0.039582	0.038798	0.153996	0.027754	0.072525	0.116735	0.214161

244 rows × 2051 columns

# Split the dataset into features and labels
X = hdf.loc[:, hdf.columns != 'short_histology'].values
y = hdf.loc[:, hdf.columns == 'short_histology'].values.ravel()

# Split PBTA data into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

#Sanity check
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

(183, 2050) (61, 2050) (183,) (61,)

# Initialize random forest classifier
rfc = RandomForestClassifier(max_depth=10, random_state=0)

# Train the random forest classifier
rfc.fit(X_train, y_train)

# Make predictions using random forest classifier
rfc_y_pred = rfc.predict(X_test)

# Accuracy of model
print(f'Accuracy: {accuracy_score(y_test, rfc_y_pred)}')

Accuracy: 0.3770491803278688

# Calculate a confusion matrix
cm = confusion_matrix(y_test, rfc_y_pred, labels=rfc.classes_)

# Display confusion matrix to look at how accurately the ML model was able to classify each tumor type
disp = px.imshow(cm, text_auto=True,
                labels=dict(x="Predicted Tumor Type", y="True Tumor Type", color="Productivity"),
                x=df['short_histology'].unique().tolist(),
                y=df['short_histology'].unique().tolist()
                )
disp.show()

# Library for UMAP projections
from umap.umap_ import UMAP

umap_2d = UMAP(n_components=2, init='random', random_state=0)
umap_3d = UMAP(n_components=3, init='random', random_state=0)

proj_2d = umap_2d.fit_transform(X)
proj_3d = umap_3d.fit_transform(X)

fig_2d = px.scatter(proj_2d, x=0, y=1, color=hdf['short_histology'].tolist())

fig_3d = px.scatter_3d(proj_3d, x=0, y=1, z=2, color=hdf['short_histology'].tolist())

fig_2d.show()
fig_3d.show()